An unmanned boat monitoring system and method suitable for multi-field scanning perception of river and lake water bodies

Abstract

The present invention discloses an unmanned boat monitoring system and method suitable for multi-field scanning perception of river and lake water bodies. The system includes a host computer, an unmanned boat, and a slave computer and various functional modules carried by the unmanned boat. The host computer controls the unmanned boat to cruise and collect data and water samples through the slave computer. The slave computer is used to exchange data with the host computer, receive instructions from the host computer to control the unmanned boat and various functional modules, and feed back various types of data perceived by each functional module to the host computer. Each functional module includes a positioning module, a wireless communication module, a data storage module, a water sample collection module, an environmental monitoring module, a power drive module, an obstacle avoidance module, an early warning indication module, and a power supply module. The system and method of the present invention can automatically perceive multiple elements of river and lake water bodies, complete the full-field traversal of river and lake water bodies by the unmanned boat, and achieve scientific interpolation of point data of river and lake water bodies into surface data under multi-factor disturbances.

Description

Technical Field

[0001] The present invention belongs to the technical field of river and lake ecological environment monitoring and protection, and in particular relates to an unmanned boat monitoring system and method suitable for multi-field scanning perception of river and lake water bodies. Background Art

[0002] The use of unmanned boats to perceive the hydrological, water quality, and aquatic ecological elements of rivers and lakes is a common method for monitoring and protecting river and lake water ecosystems. Achieving a benign interaction between the hydrodynamic, pollution, and ecological fields of rivers and lakes is fundamental to maintaining their ecological recovery. Therefore, how to use unmanned boats to accurately and efficiently perceive the hydrodynamic, pollution, and ecological fields of rivers and lakes is a key issue that urgently needs to be addressed in the field of river and lake ecological monitoring and protection technology.

[0003] Currently, there are the following difficulties in using unmanned boats to conduct real-time perception of the hydrology, water quality, and water ecological elements of rivers and lakes, as well as multi-field scanning of river and lake water bodies:

[0004] 1. The size of various monitoring sensors on the market does not match the volume of the unmanned ship cabin:

[0005] Currently, most water quality monitoring equipment on the market is cylindrical detection equipment. When mounted on an unmanned boat, it is often fixed to the hull by a fixed device or connected to the unmanned boat by a fixed-length rope. It can only monitor water quality parameters at a single fixed surface water depth and cannot perform three-dimensional monitoring of stratified water bodies. In addition, when unmanned boats are released in areas near the shores of lakes, rivers, and reservoirs, the shallow water near the shore can easily cause damage to the water quality monitoring equipment such as dragging, friction, and collision. At the same time, when the multi-parameter water quality monitoring equipment carried by the unmanned boat monitors water quality conditions, conventional mobile detection equipment does not have the ability to conduct in-depth analysis and comprehensive measurement of water quality parameters. Depending on the water quality risk status, water quality sampling may sometimes be required.

[0006] 2. Unmanned boat path planning problem in dynamic river and lake water environments:

[0007] Traditional methods typically divide rivers and lakes into multiple uniform grids based on their size. Unmanned vessels then set patrol routes within each grid unit according to a specific pattern, allowing the vessels to inspect each grid unit one by one and sense relevant elements. This approach addresses the slow efficiency and limited coverage of manual sampling and monitoring to some extent, but it also presents certain shortcomings. First, it fails to account for the dynamic environmental factors of rivers and lakes, such as flow velocity and obstacles. This makes the planned route difficult to fully implement in dynamic environments, and local dynamic conditions can easily cause the unmanned vessel's patrol route to become stuck in a loop. Second, the manually defined routes fail to fully leverage the flow field of the river or lake, simply aiming for full coverage of the water body. However, due to the limited cabin space and battery capacity of unmanned vessels, they only cover a large area of ​​the water body at once, resulting in time lags in the perception data. Therefore, efficiently and accurately determining the navigation path of unmanned vessels in dynamic river and lake environments remains a challenge.

[0008] 3. Interpolation problem of irregular multi-point monitoring data in the dynamic environment of river and lake water bodies:

[0009] Affected by uncontrollable environmental factors such as the flow velocity and wind speed of rivers and lakes, as well as the uneven speed of unmanned boats, the points of water ecological environment conditions of rivers and lakes perceived by various sensors are uneven. Therefore, for this uneven, multi-point distributed monitoring data affected by multiple environmental factors, how to form surface data from point data, and further form a multi-field scan of the hydrodynamic field, pollution field, and biological field of rivers and lakes, is the bottleneck problem currently faced by unmanned boats in monitoring the water ecological environment of rivers and lakes. Summary of the Invention

[0010] The purpose of the present invention is to provide an unmanned boat monitoring system and method suitable for multi-field scanning perception of river and lake water bodies to solve the above-mentioned technical problems.

[0011] To achieve the above object, the present invention provides the following technical solutions:

[0012] The present invention discloses an unmanned boat monitoring system suitable for multi-field scanning perception of river and lake water bodies, the system comprises a host computer, an unmanned boat, and a lower computer and various functional modules carried by the unmanned boat; the host computer controls the unmanned boat to cruise and collect data and water samples through the lower computer; the lower computer is used to exchange data with the host computer, receive instructions from the host computer to control the unmanned boat and various functional modules, and feed back various types of data perceived by various functional modules to the host computer; the various functional modules include a positioning module, a wireless communication module, a data storage module, a water sample collection module, an environmental monitoring module, a power drive module, an obstacle avoidance module, an early warning indication module and an electric Source module; the wireless communication module is used to provide wireless communication data transmission function; the positioning module is used to locate the position of the unmanned boat in real time; the data storage module is used to receive and store data in real time; the water sample collection module is used to collect water samples at different monitoring points; the environmental monitoring module is used to monitor environmental factor data at different water depths; the power drive module is used to provide power for the operation of the unmanned boat; the obstacle avoidance module is used to monitor whether there are obstacles in front of the unmanned boat; the early warning indication module is used to indicate the location of the unmanned boat in unfavorable light or actual needs; the power supply module is used to power the unmanned boat and other functional modules.

[0013] Furthermore, the water sample collection module includes a collection device and a base device that are connected to each other; the collection device includes a water pump, a water pipe and multiple sampling bottles, the water pump is connected to the water pipe, and the water pump has a built-in water stop valve; the base device includes a turntable and a rotating motor, the rotating motor is arranged in the center of the turntable for driving the turntable to rotate, and a plurality of grooves are evenly arranged on the turntable for placing and fixing each sampling bottle; the collection device extracts water samples through the water pump, and guides the water to the water outlet at a fixed position through the water pipe, and the water outlet is aligned with the bottle mouth of the sampling bottle. After each sampling by the collection device is completed, the turntable of the base device is controlled by the rotating motor to rotate a fixed angle, and the rotation angle is set according to the number of grooves, so that after each sampling is completed, the turntable automatically rotates to the next sampling bottle to align with the water outlet.

[0014] Furthermore, the environmental monitoring module includes a hydrological and water quality monitoring device and an underwater sonar monitoring device. The hydrological and water quality monitoring device includes a connected winch lifting device and a monitor; the winch lifting device includes a motor, a winch, a pulley, a cylinder and a rope. The motor is used to control the rotation of the winch. The pulley and the cylinder are fixed to the rear of the sealed cabin. The pulley is arranged above the cylinder. One end of the rope is fixedly wound around the winch, and the other end is connected to the monitor located inside the cylinder through the pulley. The cylinder is used to ensure the vertical lifting of the monitor. The upper part of the monitor is connected to the rope. During lifting, the outer wall of the monitor is stably operated against the cylinder. The motor controls the rotation of the winch to control the retraction length of the rope and thus controls the water measurement depth. The probe of the monitor is provided with a multi-parameter environmental factor perception sensor for monitoring environmental factor data at different water depths; the underwater sonar monitoring device is used for water depth monitoring, underwater terrain scanning and underwater biological image shooting.

[0015] Furthermore, the environmental factor data include flow rate, water temperature, dissolved oxygen, total suspended solids, pH, turbidity, chemical oxygen demand, ammonia nitrogen, total phosphorus, algae biomass, and ionic state.

[0016] Furthermore, the host computer includes a data processing module and a terminal display module. The data processing module is used for sending, receiving and processing data, and the terminal display module is used for displaying data results.

[0017] Furthermore, the unmanned boat includes a sealed cabin and a floating airbag wrapped around the sealed cabin, and the top of the cabin is provided with an openable and waterproof protective cover.

[0018] Furthermore, the system also includes a manual remote controller, which is used to manually control the operation path of the unmanned boat.

[0019] The present invention also discloses an unmanned boat monitoring method based on the system and applicable to multi-field scanning perception of river and lake water bodies, the method comprising the following steps:

[0020] Step 1: Place the unmanned boat on the shore of the water body to be monitored, debug the unmanned boat, lower computer and various functional modules to ensure that the unmanned boat can operate normally;

[0021] Step 2: Turn on the host computer and lower the unmanned boat into the water body to be monitored. The host computer divides the water body grid according to the range of the water body to be monitored on the electronic map. Based on the built-in unmanned boat path planning algorithm, the unmanned boat is set to travel along the entire water body to be monitored. The unmanned boat cruises along the set path.

[0022] Step 3: Every time the unmanned boat arrives at a monitoring point, it records the monitoring point number, collects water samples at different monitoring points using the water sampling module, monitors environmental element data at different water depths using the environmental monitoring module, and simultaneously scans underwater terrain and captures underwater biological images; the monitoring data of the monitoring point is transmitted to the host computer in real time through the slave computer, and is also transmitted to the data storage module for storage until the unmanned boat completes the monitoring of all monitoring points in the entire water body to be monitored;

[0023] Step 4: The host computer processes the received monitoring data, scans and renders the hydrodynamic field, pollution field, and biological field of the water body to be monitored through a built-in interpolation algorithm, and displays the rendered images of the hydrodynamic field, pollution field, and biological field of the water body to be monitored;

[0024] Step 5: After the monitoring work is completed, the host computer controls the unmanned boat to return to the shore, reclaims the unmanned boat, and shuts down the host computer.

[0025] Furthermore, the unmanned ship path planning algorithm in step 2 is a flow adaptive dynamic window planning algorithm, namely the FADWP algorithm, which is specifically:

[0026] Assume that the unmanned boat traverses the river and lake area grid R. R contains a series of grid cells, each of which has a flow velocity vector v(i, j) and an obstacle mark o(i, j). The FADWP algorithm uses the river and lake water body grid, starting point, and target point as independent variables, and the path planning result and path cost function as dependent variables. It is a nonlinear function relationship expression:

[0027] FADWP(R,S,G)={P,C} (1)

[0028] Where: R is the river and lake area grid; S is the starting point; G is the target point; P is the planned path result; C is the path cost function;

[0029] The steps to implement the algorithm are:

[0030] ① Algorithm initialization: the unmanned ship starts from the starting point S, initializes the path P = {S} and the cost C = 0;

[0031] ② Define the comprehensive cost function C, which comprehensively considers the path length of the unmanned boat, the water grid traversal time, obstacle avoidance and the influence of flow velocity. Its expression is:

[0032] C(P) = w1 * L(P) + w2 * T(P) + w3 * O(P) + w4 * V(P) (2)

[0033] Where: C(P) is the representative function value of a certain path planning result; L(P) is the path length; T(P) is the expected traversal time; O(P) is the obstacle avoidance cost; V(P) is the flow velocity impact cost, and its calculation formula is shown in formula (3); w1, w2, w3, w4 are weight coefficients;

[0034] V(P) takes into account the impact of flow velocity in each grid cell of the river and lake water body on path planning, and considers the angle between the perceived flow velocity direction and the ship's direction of travel. Its expression is:

[0035] V(p) = Σ [1 - cos(θ(v(i, j), d(i, j)))] * Δt (3)

[0036] Where: θ is the angle between the velocity vector v(i, j) and the ship's travel direction d(i, j); Δt is the time in unit (i, j); i, j represent the grid numbers of the river and lake water bodies;

[0037] ③ At each step of the unmanned boat's forward movement, the unmanned boat will evaluate the path cost function values ​​of all neighboring grid cells and select the grid cell with the smallest cost as the next direction of travel, and continue to iterate until the unmanned boat moves to the target point G.

[0038] Furthermore, the interpolation algorithm in step 4 is the Environmental Adaptive Dynamic Kriging interpolation algorithm, namely the EADK algorithm, which is specifically:

[0039] Assume that the set of monitoring points is P = {P1, P2, ..., P n}, monitoring point P i The attribute value is Z i , the target interpolation point is Q, the interpolation result of point Q is Z^Q, and the environmental factors include flow velocity U, wind direction W, and water temperature T;

[0040] The formula of the EADK algorithm is as follows:

[0041] Z^Q= Σ(λ i * Z i ) (4)

[0042] Where: i is the weight of the i-th monitoring point to the interpolation point Q, and the calculation formula is:

[0043] λ i = (μ * γ(P i , P j ) + δ * Ψ(P i , P j )) / (Σ(μ * γ(P i , P j) + δ * Ψ(P i , P j ))) (5)

[0044] Where: γ(P i ,P j ) is the semivariogram, indicating the monitoring point P i and P j The spatial correlation between them is calculated as follows:

[0045] γ(P i , P j )= (Z i - Z j )^2 / 2 + φ(U i , U j , T i , T j ) (6)

[0046] Where: Z i and Z j Indicates monitoring point P i and P j The attribute value of φ(U i ,U j ,T i ,T j ) is the environmental factor influence function, and the calculation formula is:

[0047] φ(U i , U j , T i , T j ) = αU * | U i -U j | + αT * | T i - T j | + αW * (1 -cos(θ i -θ j )) (7)

[0048] Where: αU, αT and αW are the influence coefficients of flow velocity U, wind direction W and water temperature T respectively, θ i and θ j They are monitoring points P i and P j The wind direction angle;

[0049] In formula (5), Ψ(P i ,P j ) is the dynamic weight adjustment function, and the calculation formula is:

[0050] Ψ(P i , Pj ) = exp(-β * D i,j / L) (8)

[0051] Where: D i,j is the monitoring point P i and P j The Euclidean distance between them, L is the range, which represents the maximum distance of spatial correlation, and β is the adjustment parameter;

[0052] In formula (5), μ is the Lagrange multiplier, which is used to ensure the unbiasedness of the interpolation; δ is the dynamic adjustment coefficient, which is used to balance the influence of spatial correlation and environmental factors.

[0053] The beneficial effects of the present invention are as follows:

[0054] 1. Ability to realize automatic perception of multiple elements of river and lake water bodies: This invention fully utilizes the advantages of unmanned boats such as maneuverability and automation. By carrying various functional modules, optimizing the layout structure, and using mechanical transmission devices such as winches and turntables, it can realize three-dimensional real-time perception of hydrological elements, water quality elements, and water ecological elements of river and lake water bodies. This invention can complete environmental element data monitoring at different water depths and protect the hydrological and water quality monitoring equipment from damage such as dragging, friction, and collision caused by the shallow water depth when the unmanned boat is released from the shore. At the same time, it can complete multi-point water quality sampling and water sample storage during unmanned boat water body monitoring.

[0055] 2. Ability to realize full-field traversal of river and lake water bodies: The present invention can realize full-field traversal perception of river and lake water bodies by unmanned boats under the interference of dynamic power fields and static obstacles, and complete efficient and accurate monitoring of water bodies.

[0056] 3. Able to realize multi-field scanning of river and lake water bodies: The present invention can realize the scientific interpolation of point data of river and lake water bodies into surface data under the disturbance of multiple factors, forming multi-field scanning of the hydrodynamic field, pollution field, and biological field of river and lake water bodies, so that users can fully understand the spatial distribution characteristics of water environment elements.

[0057] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is the principle block diagram of the unmanned ship monitoring system;

[0059] Figure 2 This is a top view of the overall structure of the unmanned ship monitoring system;

[0060] Figure 3 This is a side view of the overall structure of the unmanned ship monitoring system;

[0061] Figure 4This is a schematic diagram of the water sample collection module structure decomposition;

[0062] Figure 5 Schematic diagram of the structure of the hydrological and water quality monitoring device Figure 1 ;

[0063] Figure 6 Schematic diagram of the structure of the hydrological and water quality monitoring device Figure 2 .

[0064] In the figure: 1. Lower computer; 2. Positioning module; 3. Wireless communication module; 4. Data storage module; 5. Power supply module; 6. Acquisition device; 7. Base device; 8. Power drive module; 9. Winch lifting device; 10. Monitor; 11. Obstacle avoidance module; 12. Transmission antenna; 13. Early warning indication module; 14. Underwater sonar monitoring device; 15. Cabin; 16. Floating airbag; 20. Motor; 21. Winch; 22. Pulley; 23. Cylinder; 24. Rope; 25. Water pump; 26. Rotating motor; 27. Water stop valve; 28. Groove; 29. ​​Water pipe; 30. Sampling bottle. DETAILED DESCRIPTION

[0065] The present invention discloses an unmanned boat monitoring system suitable for multi-field scanning perception of river and lake water bodies, such as Figures 1 to 6 As shown, the system includes a host computer, an unmanned boat, and a slave computer 1 carried by the unmanned boat, along with various functional modules. The host computer controls the unmanned boat's navigation and data and water sample collection through the slave computer. The slave computer is used to exchange data with the host computer, accepting instructions from the host computer to control the unmanned boat and various functional modules, and feeding back various data sensed by each functional module to the host computer. The functional modules include a positioning module 2, a wireless communication module 3, a data storage module 4, a water sample collection module, an environmental monitoring module, a power drive module 8, an obstacle avoidance module 11, a warning indicator module 13, and a power supply module 5. The system also includes a manual remote control for manually controlling the unmanned boat's path. Generally, in emergency situations, human intervention in the unmanned boat's path is required to prevent accidents such as collisions and capsizing.

[0066] The host computer includes a data processing module and a terminal display module. The data processing module is used to send, receive, and process data, while the terminal display module is used to display data results. The host computer generally consists of a computer or mobile phone and a dedicated app installed. The app contains an electronic map and various control instructions. During use, the host computer controls the system through the app and sends instructions to the slave computer. The slave computer then sets the unmanned boat's cruise route, cruise mode, monitoring points, water sampling, and environmental factor data monitoring. When the unmanned boat reaches a monitoring point for monitoring, the slave computer feeds the monitoring data back to the host computer, which processes the received data and displays the results on the screen. The computer data processing module uses a Core i7 or higher processor or processor of equivalent performance, while the mobile phone data processing module uses a third-generation Snapdragon 7 or higher processor or processor of equivalent performance. They have 4GB or more of memory and a 100GB or more hard drive. The terminal display module uses an integrated or discrete graphics card with 512MB or more of video memory.

[0067] The unmanned boat consists of a sealed cabin 15 and a floating airbag 16 surrounding the cabin. The cabin is topped with an openable, waterproof protective cover. The cabin is constructed from high-strength fiberglass composite materials or high-strength, high-performance alloys. The floating airbag 16 is made of reinforced polyethylene, which is resistant to corrosion, freezing, oxidation, and cyanosis. The hull airbag consists of two parts, which are inflated during use. The floating airbag encloses the sealed cabin, providing buoyancy and reducing collision damage to the hull, thus protecting the unmanned boat. It is typically brightly colored, such as orange or red, for easy identification over large bodies of water.

[0068] Slave computer 1, located within the unmanned vessel's cabin, is responsible for data transfer and command distribution, interacting with the host computer. Powered by an ARM-Cortex-M3 core, it integrates an electronic speed regulator and an MPU6000 module. After receiving commands from the host computer or a manual remote control, the slave computer decompresses and dispatches them, controlling the vessel's movement and monitoring the water. The slave computer also compresses and encrypts the monitoring data from each module before transmitting it to the host computer. The data transmission frequency can be set to 10s, 20s, or 30s, depending on monitoring requirements.

[0069] Positioning module 2 is used to locate the unmanned vessel's monitoring position in real time and transmit the positioning data to the lower computer. This invention uses a GPS / BDS (Beidou) positioning module, which is integrated with a GPS positioning module and a BDS positioning module. It can switch between GPS and BDS positioning modes and has the advantages of high positioning accuracy, high speed, and low energy consumption.

[0070] The wireless communication module 3 provides wireless data transmission, enabling lower computer 1 to communicate with the upper computer via the wireless communication module. The present invention utilizes a GSM / GPRS wireless communication module, which provides stable, high-speed, and long-distance data transmission services. The transmission antenna 12 of the wireless communication module 3 is an antenna assembly located at the center front of the cabin roof, ensuring efficient and timely signal transmission and reception.

[0071] The data storage module 4 is used to receive and store various monitoring data, positioning data, etc. transmitted by the lower computer in real time. The present invention adopts a large-capacity portable data storage device and adopts SD card storage technology to realize data collection and storage. It has the characteristics of simple structure, low power consumption (module power consumption ≤ 0.3W), stable performance, small size and low cost. It adopts a 32-bit / 64-bit high-performance ARM processor with fast processing speed and stable performance; the data storage type is EXCEL / TXT file, which is convenient for software batch processing; it has a data storage interface and a system control interface; it has a built-in power supply and can work continuously for more than 24 hours when fully charged; it has strong anti-interference ability and is suitable for complex meteorological and hydrological conditions. The power supply of the data storage is provided by the built-in power supply and the power module. The power is first supplied by the power module. When the power module is insufficient, the built-in power supply supplies power to the data storage device.

[0072] The water sampling module is used to collect water samples at different monitoring points. It includes a collection device 6 and a base device 7, which are connected to each other. The collection device 6 includes a water pump 25, a water conduit 29, and multiple sampling bottles 30. The water pump is connected to the water conduit and has a built-in water stop valve 27 to prevent backflow. The base device 7 includes a turntable and a rotating motor 26, typically a 5V DC motor. The motor is located in the center of the turntable and drives the turntable. The turntable is evenly distributed with multiple grooves 28 for holding and securing the sampling bottles 30. The collection device extracts water samples through the water pump 25 and directs the water through the water conduit 29 to a fixed outlet, which is aligned with the mouth of the sampling bottle. After each sampling operation, the motor controls the base device's turntable to rotate by a fixed angle. The rotation angle is set according to the number of grooves (for example, 60 degrees for six grooves and 45 degrees for eight grooves). After each sampling operation, the turntable automatically rotates to the next sampling bottle, aligning it with the water outlet. The base device has perfect braking and precise adjustment of the rotation angle function.

[0073] The turntable is provided with X (X is an even number, preferably 6 or 8) grooves, which are filled with blank sampling bottles. By default, it is aimed at a certain sampling bottle. The default water sampling volume of the sampling bottle is V, and the water pumping flow rate is Q. When in use, the host computer sets the sampling time to V / Q. When the sampling is completed, the host computer records the sampled amount as Y, and the motor automatically controls the rotation angle of the turntable to 360 / X degrees. The above operation is repeated when it reaches the next sampling point.

[0074] The environmental monitoring module includes a hydrological and water quality monitoring device and an underwater sonar monitoring device 14. The hydrological and water quality monitoring device is used to monitor environmental factor data at different water depths. The hydrological and water quality monitoring device includes a connected winch hoisting device 9 and a monitor 10. The winch hoisting device includes a motor 20, a winch 21, a pulley 22, a cylinder 23 and a rope 24. The motor is used to control the rotation of the winch. The pulley and the cylinder are fixed to the rear of the sealed cabin. The pulley is set above the cylinder. One end of the rope is fixedly wound around the winch, and the other end is pulled through the pulley. The wheel connects to the monitor located inside the cylinder, which ensures vertical lifting of the monitor. The upper portion of the monitor is connected to a winch, which stabilizes the monitor against the cylinder during lifting. A motor controls the rotation of the winch to adjust the length of the winch, thereby controlling the water depth. The monitor's probe is equipped with a multi-parameter environmental sensor to monitor environmental factors at different water depths, including flow rate, water temperature, dissolved oxygen, total suspended solids, pH, turbidity, chemical oxygen demand, ammonia nitrogen, total phosphorus, algal biomass, and ionic state. The monitor has a built-in power supply, ensuring long-term continuous use.

[0075] The rope is usually made of nylon rope, steel cable or hemp rope. The rope is strong and thin, and the length of each circle wrapped around the winch will not change significantly due to multiple windings. The radius of the winch is R, and the circumference of a circle of rope is πR 2 In the initial position, the monitor probe is located at the interface between the water surface and the air. When the water quality and hydrology of a water body at an underwater depth H need to be measured, the host computer inputs the measurement depth H, and the host computer automatically calculates and controls the motor shaft to rotate 180H / πR degrees. When it stabilizes, the data is read to complete the hydrological and water quality measurement of the water body at a depth of H.

[0076] The underwater sonar monitoring device 14 is used to monitor water depth, scan underwater terrain, and capture images of underwater organisms, and transmit the data to the lower computer, which is then processed by the lower computer and fed back to the upper computer. The present invention uses Lawrence 3D stereo sonar monitoring equipment, which consists of a 3D wide-angle high-definition probe and a recording and display device. It can support a maximum scanning depth of 100 meters and a scanning range of 200 meters on each side. Through sonar feedback signal processing, it has the functions of rapid target recognition and high-definition wide-angle 3D scanning. It can rotate and replay the recorded stereo images, view and record the underwater landform structure from any angle, and simultaneously scan and image medium and large aquatic plants and various animals. It can complete the mapping of topographic maps and the monitoring and recording of the biomass of medium and large aquatic plants and animals.

[0077] The power drive module 8 is used to provide power for the hull operation. The power drive module consists of two AC (DC) motors and two jet thrusters. The AC (DC) motors are connected to the lower computer, which controls the speed of the left and right motors to control the hull's obstacle avoidance and operation.

[0078] The obstacle avoidance module 11 monitors the presence of obstacles ahead of the unmanned boat and transmits this data to the host computer. This module, implemented using an HC series ultrasonic ranging module, is located at the bow of the unmanned boat and monitors the presence of obstacles in real time. If the obstacle is within 3 meters, the boat automatically slows down and steers to avoid it, generating an alarm on the host computer.

[0079] The warning indicator module 13 facilitates monitoring personnel to quickly locate the unmanned vessel in adverse lighting conditions or when needed. It includes a switch controller and indicator lights. During actual monitoring, the indicator lights can be remotely controlled to turn on and off based on lighting conditions and actual needs.

[0080] The above-mentioned unmanned boat monitoring system fully solves the problems of the incompatibility between the currently commonly used environmental monitoring modules and the volume of the unmanned boat cabin and the inability to automatically switch the water sample collection module. It increases the diversity of monitoring locations and helps to complete the three-dimensional monitoring of water bodies; it can realize the automatic collection of water samples at different monitoring points.

[0081] The present invention also discloses an unmanned boat monitoring method applicable to multi-field scanning perception of river and lake water bodies, the method comprising the following steps:

[0082] Step 1: Place the unmanned boat on the shore of the water body to be monitored (including rivers, lakes, reservoirs, etc.), debug the unmanned boat, lower computer and various functional modules to ensure that the unmanned boat can operate normally.

[0083] Step 2: Turn on the host computer and lower the unmanned boat into the water body to be monitored. The host computer divides the water body grid according to the range of the water body to be monitored on the electronic map. According to the built-in unmanned boat path planning algorithm, the unmanned boat operation path is set so that it traverses the entire water body to be monitored. The unmanned boat cruises along the set operation path.

[0084] The main factors affecting the path planning of unmanned boats in rivers and lakes are dynamic flow velocity and static obstacles. In particular, the change of dynamic flow velocity has a greater impact on the path planning of unmanned boats. This paper fully considers the impact of the dynamic power field of river and lake water bodies on the path planning of unmanned boats and proposes an unmanned boat path planning algorithm that takes into account the dynamic flow velocity and static obstacles of the water body, namely the flow-adaptive dynamic window planning (FADWP) algorithm. Specifically,

[0085] Assume that the unmanned boat traverses the river and lake area grid R. R contains a series of grid cells, each of which has a flow velocity vector v(i, j) and an obstacle mark o(i, j). The FADWP algorithm uses the river and lake water body grid, starting point, and target point as independent variables, and the path planning result and path cost function as dependent variables. It is a nonlinear function relationship expression:

[0086] FADWP(R,S,G)={P,C} (1)

[0087] Where: R is the river and lake area grid; S is the starting point; G is the target point; P is the planned path result; C is the path cost function;

[0088] The steps to implement the algorithm are:

[0089] ① Algorithm initialization: the unmanned ship starts from the starting point S, initializes the path P = {S} and the cost C = 0;

[0090] ② Define the comprehensive cost function C, which comprehensively considers the path length of the unmanned boat, the water grid traversal time, obstacle avoidance and the influence of flow velocity. Its expression is:

[0091] C(P) = w1 * L(P) + w2 * T(P) + w3 * O(P) + w4 * V(P) (2)

[0092] Where: C(P) is the representative function value of a certain path planning result; L(P) is the path length; T(P) is the expected traversal time; O(P) is the obstacle avoidance cost; V(P) is the flow velocity impact cost, and its calculation formula is shown in formula (3); w1, w2, w3, w4 are weight coefficients;

[0093] V(P) takes into account the impact of flow velocity in each grid cell of the river and lake water body on path planning, and considers the angle between the perceived flow velocity direction and the ship's direction of travel. Its expression is:

[0094] V(p) = Σ [1 - cos(θ(v(i, j), d(i, j)))] * Δt (3)

[0095] Where: θ is the angle between the velocity vector v(i, j) and the ship's travel direction d(i, j); Δt is the time in unit (i, j); i, j represent the grid numbers of the river and lake water bodies;

[0096] ③ At each step of the unmanned boat's forward movement, the unmanned boat will evaluate the path cost function values ​​of all neighboring grid cells and select the grid cell with the smallest cost as the next direction of travel, and continue to iterate until the unmanned boat moves to the target point G.

[0097] The pseudo code to implement this algorithm is as follows:

[0098]

[0099] Compared to conventional static path planning algorithms for unmanned vessels, this algorithm incorporates factors such as path length, water grid traversal time, obstacle avoidance, and flow velocity into its path cost function, making it suitable for path planning in the dynamic environments of rivers and lakes. In practical applications, the weight coefficients and cost function can be adjusted to suit different river and lake environments and the characteristics of unmanned vessels.

[0100] Step 3: Every time the unmanned boat arrives at a monitoring point, it records the monitoring point number, collects water samples at different monitoring points using the water sampling module, monitors environmental element data at different water depths using the environmental monitoring module, and simultaneously scans underwater terrain and captures underwater biological images; the monitoring data of the monitoring point is transmitted to the host computer in real time through the slave computer, and is also transmitted to the data storage module for storage until the unmanned boat completes the monitoring of all monitoring points in the entire water body to be monitored;

[0101] Step 4: The host computer processes the received monitoring data and scans and renders the hydrodynamic field, pollution field and biological field of the water body to be monitored through the built-in interpolation algorithm, obtains and displays the renderings of the hydrodynamic field, pollution field and biological field of the water body to be monitored.

[0102] The built-in interpolation algorithm is the Environmental Adaptability Dynamic Kriging interpolation (EADK) algorithm, which fully considers environmental factors such as flow velocity U, wind direction W, and water temperature T. The specific algorithm is:

[0103] Assume that the set of monitoring points is P = {P1, P2, ..., P n}, monitoring point P i The attribute value is Z i , the target interpolation point is Q, the interpolation result of point Q is Z^Q, and the environmental factors include flow velocity U, wind direction W, and water temperature T;

[0104] The formula of the EADK algorithm is as follows:

[0105] Z^Q= Σ(λ i * Z i ) (4)

[0106] Where: i is the weight of the i-th monitoring point to the interpolation point Q, and the calculation formula is:

[0107] λ i = (μ * γ(P i , P j ) + δ * Ψ(P i , P j)) / (Σ(μ * γ(P i , P j ) + δ * Ψ(P i , P j ))) (5)

[0108] Where: γ(P i ,P j ) is the semivariogram, indicating the monitoring point P i and P j The spatial correlation between them is calculated as follows:

[0109] γ(P i , P j )= (Z i - Z j )^2 / 2 + φ(U i , U j , T i , T j ) (6)

[0110] Where: Z i and Z j Indicates monitoring point P i and P j The attribute value of φ(U i ,U j ,T i ,T j ) is the environmental factor influence function, and the calculation formula is:

[0111] φ(U i , U j , T i , T j ) = αU * | U i -U j | + αT * | T i - T j | + αW * (1 -cos(θ i -θ j )) (7)

[0112] Where: αU, αT and αW are the influence coefficients of flow velocity U, wind direction W and water temperature T respectively, θ i and θ j They are monitoring points P i and P j The wind direction angle;

[0113] In formula (5), Ψ(P i ,P j ) is the dynamic weight adjustment function, and the calculation formula is:

[0114] Ψ(P i , P j ) = exp(-β * D i,j / L) (8)

[0115] Where: D i,j is the monitoring point P i and P j The Euclidean distance between them, L is the range, which represents the maximum distance of spatial correlation, and β is the adjustment parameter;

[0116] In formula (5), μ is the Lagrange multiplier, which is used to ensure the unbiasedness of the interpolation; δ is the dynamic adjustment coefficient, which is used to balance the influence of spatial correlation and environmental factors.

[0117] The EADK algorithm takes into account the influence of the dynamic flow velocity of the water body and the distance between points. Compared with traditional inverse distance interpolation, Kriging interpolation and other algorithms, this algorithm fully considers the influence of environmental factors such as the distance between points, flow velocity, wind direction, and water temperature on different monitoring points when assigning weights to the monitoring values ​​of different monitoring points. It is suitable for unmanned boats to scan hydrodynamic fields, pollution fields, and biological fields in the dynamic environment of river and lake water bodies, and can more accurately estimate the spatial distribution characteristics of irregular point monitoring data of river and lake water bodies.

[0118] Step 6: After the monitoring is complete, the host computer controls the unmanned boat to return to shore, retrieve the unmanned boat, and shut down the host computer. By using the unmanned boat monitoring system and following the above steps, users can master the unmanned boat's cruising path and fully understand the spatial distribution characteristics of the water environment elements to be monitored.

[0119] Finally, it should be noted that the above description is only used to illustrate the technical solution of the present invention and is not intended to limit it. Although the present invention has been described in detail with reference to the preferred arrangement scheme, those skilled in the art should understand that the technical solution of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An unmanned boat monitoring method suitable for multi-field scanning perception of river and lake water bodies, the method is based on an unmanned boat monitoring system, characterized in that: The system includes a host computer, an unmanned boat, and a slave computer and various functional modules carried by the unmanned boat; the host computer controls the unmanned boat to cruise and collect data and water samples through the slave computer; the slave computer is used to exchange data with the host computer, accept instructions issued by the host computer to control the unmanned boat and various functional modules, and feed back various types of data perceived by each functional module to the host computer; the functional modules include a positioning module, a wireless communication module, a data storage module, a water sample collection module, an environmental monitoring module, a power drive module, an obstacle avoidance module, an early warning indication module and a power supply module; the wireless communication module is used to provide wireless communication data transmission function; the positioning module is used to locate the position of the unmanned boat in real time; the data storage module is used to receive and store data in real time; the water sample collection module is used to collect water samples at different monitoring points; the environmental monitoring module is used to monitor environmental factor data at different water depths; the power drive module is used to provide power for the operation of the unmanned boat; the obstacle avoidance module is used to monitor whether there are obstacles in front of the unmanned boat; the early warning indication module is used to indicate the location of the unmanned boat under unfavorable light or actual needs; the power supply module is used to power the unmanned boat and other functional modules; The method comprises the following steps: Step 1: Place the unmanned boat on the shore of the water body to be monitored, debug the unmanned boat, lower computer and various functional modules to ensure that the unmanned boat can operate normally; Step 2: Turn on the host computer and lower the unmanned boat into the water body to be monitored. The host computer divides the water body grid according to the range of the water body to be monitored on the electronic map. Based on the built-in unmanned boat path planning algorithm, the unmanned boat is set to travel along the entire water body to be monitored. The unmanned boat cruises along the set path. The unmanned ship path planning algorithm is the flow adaptive dynamic window planning algorithm, namely the FADWP algorithm, which is specifically: Assume that the unmanned boat traverses the river and lake area grid R. R contains a series of grid cells, each of which has a flow velocity vector v(i, j) and an obstacle mark o(i, j). The FADWP algorithm uses the river and lake water body grid, starting point, and target point as independent variables, and the path planning result and path cost function as dependent variables. It is a nonlinear function relationship expression: FADWP(R,S,G)={P,C} (1) Where: R is the river and lake area grid; S is the starting point; G is the target point; P is the planned path result; C is the path cost function; The steps to implement the algorithm are: ① Algorithm initialization: the unmanned ship starts from the starting point S, initializes the path P = {S} and the cost C = 0; ② Define the comprehensive cost function C, which comprehensively considers the path length of the unmanned boat, the water grid traversal time, obstacle avoidance and the influence of flow velocity. Its expression is: C(P) = w1 * L(P) + w2 * T(P) + w3 * O(P) + w4 * V(P) (2) Where: C(P) is the representative function value of a certain path planning result; L(P) is the path length; T(P) is the expected traversal time; O(P) is the obstacle avoidance cost; V(P) is the flow velocity impact cost, and its calculation formula is shown in formula (3); w1, w2, w3, w4 are weight coefficients; V(P) takes into account the impact of flow velocity in each grid cell of the river and lake water body on path planning, and considers the angle between the perceived flow velocity direction and the ship's direction of travel. Its expression is: V(p) = Σ [1 - cos(θ(v(i, j), d(i, j)))] * Δt (3) Where: θ is the angle between the velocity vector v(i, j) and the ship's travel direction d(i, j); Δt is the time in unit (i, j); i, j represent the grid numbers of the river and lake water bodies; ③ At each step of the unmanned boat's forward movement, the unmanned boat will evaluate the path cost function values ​​of all neighboring grid cells and select the grid cell with the smallest cost as the next moving direction, and continue to iterate until the unmanned boat moves to the target point G; Step 3: Every time the unmanned boat arrives at a monitoring point, it records the monitoring point number, collects water samples at different monitoring points using the water sampling module, monitors environmental element data at different water depths using the environmental monitoring module, and simultaneously scans underwater terrain and captures underwater biological images; the monitoring data of the monitoring point is transmitted to the host computer in real time through the slave computer, and is also transmitted to the data storage module for storage until the unmanned boat completes the monitoring of all monitoring points in the entire water body to be monitored; Step 4: The host computer processes the received monitoring data, scans and renders the hydrodynamic field, pollution field, and biological field of the water body to be monitored through a built-in interpolation algorithm, and displays the rendered images of the hydrodynamic field, pollution field, and biological field of the water body to be monitored; Step 5: After the monitoring work is completed, the host computer controls the unmanned boat to return to the shore, reclaims the unmanned boat, and shuts down the host computer.

2. The unmanned boat monitoring method for multi-field scanning perception of river and lake water bodies according to claim 1 is characterized in that: The interpolation algorithm in step 4 is the Environmental Adaptive Dynamic Kriging interpolation algorithm, namely the EADK algorithm, which is specifically: Assume that the set of monitoring points is P = {P1, P2, ..., P n }, monitoring point P i The attribute value is Z i , the target interpolation point is Q, the interpolation result of point Q is Z^Q, and the environmental factors include flow velocity U, wind direction W, and water temperature T; The formula of the EADK algorithm is as follows: Z^Q= Σ(λ i * WITH i ) (4) Where: i is the weight of the i-th monitoring point to the interpolation point Q, and the calculation formula is: l i = (μ * γ(P i , P j ) + δ * Ψ(P i , P j )) / (Σ(μ * γ(P i , P j ) + δ * Ψ(P i ,P j ))) (5) Where: γ(P i ,P j ) is the semivariogram, indicating the monitoring point P i and P j The spatial correlation between them is calculated as follows: γ(P i , P j )= (Z i - Z j )^2 / 2 + φ(U i , U j , T i , T j ) (6) Where: Z i and Z j Indicates monitoring point P i and P j The attribute value of φ(U i ,U j ,T i ,T j ) is the environmental factor influence function, and the calculation formula is: φ(U i , U j , T i , T j ) = αU * | U i - U j | + αT * | T i - T j | + αW * (1 - cos(θ i - i j )) (7) Where: αU, αT and αW are the influence coefficients of flow velocity U, wind direction W and water temperature T respectively, θ i and θ j They are monitoring points P i and P j The wind direction angle; In formula (5), Ψ(P i ,P j ) is the dynamic weight adjustment function, and the calculation formula is: Ψ(P i , P j ) = exp(-β * D i,j / L) (8) Where: D i,j is the monitoring point P i and P j The Euclidean distance between them, L is the range, which represents the maximum distance of spatial correlation, and β is the adjustment parameter; In formula (5), μ is the Lagrange multiplier, which is used to ensure the unbiasedness of the interpolation; δ is the dynamic adjustment coefficient, which is used to balance the influence of spatial correlation and environmental factors.

3. The unmanned boat monitoring method for multi-field scanning perception of river and lake water bodies according to claim 1 is characterized in that: The water sample collection module includes a collection device and a base device that are connected to each other; the collection device includes a water pump, a water pipe and multiple sampling bottles, the water pump is connected to the water pipe, and the water pump has a built-in water stop valve; the base device includes a turntable and a rotating motor, the rotating motor is arranged in the center of the turntable for driving the turntable to rotate, and a plurality of grooves are evenly arranged on the turntable for placing and fixing each sampling bottle; the collection device extracts water samples through the water pump, and guides the water to a water outlet at a fixed position through the water pipe, and the water outlet is aligned with the bottle mouth of the sampling bottle. After each sampling by the collection device is completed, the turntable of the base device is controlled by the rotating motor to rotate a fixed angle, and the rotation angle is set according to the number of grooves, so that after each sampling is completed, the turntable automatically rotates to the next sampling bottle to align with the water outlet.

4. The unmanned boat monitoring method for multi-field scanning perception of river and lake water bodies according to claim 1 is characterized in that: The environmental monitoring module includes a hydrological and water quality monitoring device and an underwater sonar monitoring device. The hydrological and water quality monitoring device includes a connected winch lifting device and a monitor; the winch lifting device includes a motor, a winch, a pulley, a cylinder and a rope. The motor is used to control the rotation of the winch. The pulley and the cylinder are fixed to the rear of the sealed cabin. The pulley is arranged above the cylinder. One end of the rope is fixedly wound around the winch, and the other end is connected to the monitor located inside the cylinder through the pulley. The cylinder is used to ensure the vertical lifting of the monitor. The upper part of the monitor is connected to the rope. When lifting, the outer wall of the monitor is stably operated against the cylinder. The motor controls the rotation of the winch to control the retraction length of the rope and thus controls the water measurement depth. The probe of the monitor is provided with a multi-parameter environmental factor perception sensor for monitoring environmental factor data at different water depths; the underwater sonar monitoring device is used to perform water depth monitoring, underwater terrain scanning and underwater biological image shooting.

5. The unmanned boat monitoring method for multi-field scanning perception of river and lake water bodies according to claim 1 is characterized in that: The environmental factor data include flow rate, water temperature, dissolved oxygen, total suspended solids, pH, turbidity, chemical oxygen demand, ammonia nitrogen, total phosphorus, algae biomass, and ionic state.

6. The unmanned boat monitoring method for multi-field scanning perception of river and lake water bodies according to claim 1 is characterized in that: The host computer includes a data processing module and a terminal display module. The data processing module is used for sending, receiving and processing data, and the terminal display module is used for displaying data results.

7. The unmanned boat monitoring method for multi-field scanning perception of river and lake water bodies according to claim 1 is characterized in that: The unmanned boat comprises a sealed cabin and a floating airbag wrapped around the sealed cabin. An openable and waterproof protective cover is provided on the top of the cabin.

8. The unmanned boat monitoring method for multi-field scanning perception of river and lake water bodies according to claim 1 is characterized in that: The system also includes a manual remote controller, which is used to manually control the running path of the unmanned boat.

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